Real-time retail out-of-shelf detection

Abstract

A robust, data-driven model for detecting Out-of-Shelf (OOS) events in retail environments in real-time. Utilizing transactional logs, the system employs a statistical model to analyze sales data across specific intervals, identifying significant deviations from expected sales patterns. By harnessing noise within the data, the model generates a probability density function for each item, facilitating the detection of unlikely sales drops. Alerts are triggered when sales fall below a predefined significance level, enabling immediate remedial action. This innovative approach offers a cost-effective, scalable solution to minimize sales interruptions and enhance item inventory management.

1 . A method, comprising:
 collecting historical transaction data from a plurality of retail stores;   generating a probability distribution function (PDF) for at least one item based on the historical transaction data;   continuously receiving real-time transaction data relevant to sales of the at least one item from a particular retail store;   comparing real-time sales data for the at least one item against the PDF to detect deviations in item sales totals; and   triggering an alert when the real-time sales data deviates according to the PDF beyond a predetermined threshold indicating a potential item sales interruption event with respect to the at least one item at the particular retail store.   
     
     
         2 . The method of  claim 1 , further comprising updating the PDF dynamically when the historical transaction data is updated. 
     
     
         3 . The method of  claim 1 , further comprising implementing and providing the method as a cloud-based service to retail systems associated with the retail stores. 
     
     
         4 . The method of  claim 1 , wherein collecting further includes identifying from the historical transaction data and for each transaction in the historical transaction data, an item identifier, item quantity sold, store identifier, date, time of day, and day of week. 
     
     
         5 . The method of  claim 1 , wherein collecting further includes collecting the historical transaction data from a previous period relative to a current date and extending back at least three months. 
     
     
         6 . The method of  claim 1 , wherein generating further includes using a Gaussian Kernel-Density Estimate algorithm to generate the PDF. 
     
     
         7 . The method of  claim 1 , wherein generating further includes updating the PDF dynamically based on newly received historical transaction data. 
     
     
         8 . The method of  claim 1 , wherein continuously receiving further includes receiving the real-time transaction data every 15 minutes. 
     
     
         9 . The method of  claim 1 , wherein triggering further includes setting the predetermined threshold to an operating parameter, wherein the operating parameter is 5% or less. 
     
     
         10 . The method of  claim 1 , wherein triggering further includes causing an automatic inventory check on the at least one item with an inventory system associated with the particular store based on the alert. 
     
     
         11 . The method of  claim 1 , wherein triggering further includes sending the alert to at least one retail system associated with the particular store. 
     
     
         12 . The method of  claim 1 , wherein triggering further includes providing alert data with the alert, wherein the alert data includes an item identifier for the at least one item, time of the alert, and an expected item sales total versus an actual item sales total. 
     
     
         13 . A method, comprising:
 receiving ongoing transaction data for items sold at a retail store;   analyzing the transaction data to identify changes in item sales patterns for at least one item of the retail store;   generating a probability distribution function (PDF) based on identified changes to reflect current item sales trends for the at least one item; and   utilizing the PDF for real time out-of-shelf (OOS) event detection for the retail store with respect to the at least one item.   
     
     
         14 . The method of  claim 13 , wherein generating further includes randomly sampling the transaction data based on a current time of day and a current day of week to generate the PDF for a training data set that comprises randomly sampled transaction data. 
     
     
         15 . The method of  claim 14 , wherein randomly sampling further includes utilizing the training data set for generating additional PDFs associated with additional items of the retail store and utilizing each additional PDF for additional OOS event detections with respect to each of the additional items. 
     
     
         16 . The method of  claim 13 , wherein utilizing further includes using an outputted item sales total probability provided by the PDF for the at least one item in a current evaluated interval of time to identify and detect a particular OOS event as an unexpected increase or decrease in current item sales of the at least one item during the current evaluated interval of time. 
     
     
         17 . The method of  claim 13 , wherein utilizing further includes sending an alert based on a detected OOS event for the at least one item to one or more of a user interface and a retail system associated with the retail store. 
     
     
         18 . The method of  claim 13 , further comprising updating the transaction data and the PDF at configurable intervals of time. 
     
     
         19 . A system, comprising:
 at least one processor configured to execute instructions from a non-transitory computer-readable storage medium; and   the instructions when executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least processor to perform operations comprising:
 continuously collecting historical transaction data and real-time transaction data from transaction logs associated with a retail store using an API to interact with a transaction system and terminals of the retail store; 
 randomly sampling the historical transaction data based on a current time of day and a current day of week for a current interval of time to create a training data set from the randomly sampled data; 
 generating at least one probability distribution function (PDF) for item sales totals for at least one item associated with the retail store based on the training data set; 
 providing at least one current item sales total obtained from the real-time transaction data for a sub interval of time within the current interval of time as input to the at least one PDF; 
 receiving at least one item sales total probability for the at least one item as output from the at least one PDF; 
 comparing the at least one item sales total probability against a predefined threshold; and 
 sending at least one alert to at least a user interface associated with the retail store when the at least one item sales total probability falls below or is equal to the threshold as an indication that the at least one item is associated with at least one out-of-shelf (OOS) event that needs addressed by the retailer. 
   
     
     
         20 . The system of  claim 19 , wherein the instructions further cause the at least one processor to perform additional operations comprising:
 iterating to the continuously collecting at a third interval of time for updating the historical transaction data, updating the training data set, and updating the at least one PDF.